bio-workflows-crispr-editing-pipeline

Orchestrates an end-to-end CRISPR editing experiment design from target gene to delivery-ready, validatable constructs. Sequences guide design, off-target assessment, edit-modality selection (knockout, base editing, prime editing, HDR knock-in), and template/donor design, with a QC checkpoint at each handoff. Use when designing a complete CRISPR experiment for knockout, point correction, or tagging and the order of operations, the modality decision, and the cross-cutting traps are needed rather than a single step. Defers each step's mechanics to the genome-engineering skills.

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Repositório: PKU-YuanGroup/OpenAI4S
Última atividade na origem: 21 de agosto de 2026 às 05:05
Idioma detectado do SKILL.md: inglês
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Forks: 45

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CRISPR Editing Pipeline

"Design a complete CRISPR editing experiment for my target" -> Run guide design -> off-target assessment -> edit-modality selection -> template/donor design -> validation, applying a QC checkpoint at each handoff and routing every mechanic to the relevant genome-engineering skill.

Python: orchestrate the stages; enumerate/filter candidate guides with Bio.Seq

CLI/web: CRISPOR (on-target + off-target), Cas-OFFinder/CRISPRme (off-target), BE-Hive, PRIDICT

The Single Most Important Modern Insight -- the pipeline is a chain of handoffs, each with a checkpoint, and the pivotal decision is the edit modality

A CRISPR experiment fails most often not at one step but at a handoff where an unstated assumption carries through: a guide picked by on-target score that turns out non-specific, an "efficient" guide that never knocks out the protein, a base edit reported by efficiency that is a genotype soup, an HDR donor with no blocking mutation whose edit is silently re-cut. The workflow's job is to make each handoff explicit and gated. The pivotal branch is which edit modality: a transition (C->T/A->G) is usually a base-editing job; any other small precise edit is prime editing; a knockout is a plain nuclease; a large or non-transition insertion is HDR (or PE+integrase). Choosing the modality first reframes every downstream step. The cross-cutting traps the checkpoints exist to catch: on-target activity != specificity (two separate axes), efficient editing != knockout (frameshift fraction and NMD-competent exon biology decide it), base-editor efficiency != purity (bystanders), a donor without a blocking mutation self-destructs (re-cutting reads out as failed HDR), and predicted != detected != validated for off-targets.

CRISPR Editing Pipeline

Python: orchestrate the stages; enumerate/filter candidate guides with Bio.Seq

CLI/web: CRISPOR (on-target + off-target), Cas-OFFinder/CRISPRme (off-target), BE-Hive, PRIDICT

The Single Most Important Modern Insight -- the pipeline is a chain of handoffs, each with a checkpoint, and the pivotal decision is the edit modality

name	bio-workflows-crispr-editing-pipeline
description	Orchestrates an end-to-end CRISPR editing experiment design from target gene to delivery-ready, validatable constructs. Sequences guide design, off-target assessment, edit-modality selection (knockout, base editing, prime editing, HDR knock-in), and template/donor design, with a QC checkpoint at each handoff. Use when designing a complete CRISPR experiment for knockout, point correction, or tagging and the order of operations, the modality decision, and the cross-cutting traps are needed rather than a single step. Defers each step's mechanics to the genome-engineering skills.
workflow	true
depends_on	["genome-engineering/grna-design","genome-engineering/off-target-prediction","genome-engineering/base-editing-design","genome-engineering/prime-editing-design","genome-engineering/hdr-template-design"]
qc_checkpoints	[{"after_grna_design":"Context-valid on-target shortlist (CRISPOR: Rule Set 2 for U6/lentiviral, CRISPRscan for T7/embryo); reject TTTT and GC extremes; rank by predicted frameshift/out-of-frame fraction, not raw activity; carry 3-6 guides in an early constitutive NMD-competent exon"},{"after_offtarget":"Escalate predicted -> detected -> validated; reject guides with a low-mismatch high-CFD off-target in a gene; variant-aware (gnomAD) for therapeutic guides; high-fidelity nuclease in the delivery format used"},{"after_template":"Blocking (PAM/seed) mutation present AND codon-checked; edit within ~10 bp of the cut; donor format matches cell type (ssODN/lssDNA/dsDNA/AAV; HITI for post-mitotic); report edit:indel purity for base editing"}]
origin	openai4s
category	bioskills/workflows
metadata	{"tool_type":"mixed","primary_tool":"CRISPOR","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

name	bio-workflows-crispr-editing-pipeline
description	Orchestrates an end-to-end CRISPR editing experiment design from target gene to delivery-ready, validatable constructs. Sequences guide design, off-target assessment, edit-modality selection (knockout, base editing, prime editing, HDR knock-in), and template/donor design, with a QC checkpoint at each handoff. Use when designing a complete CRISPR experiment for knockout, point correction, or tagging and the order of operations, the modality decision, and the cross-cutting traps are needed rather than a single step. Defers each step's mechanics to the genome-engineering skills.
workflow	true
depends_on	["genome-engineering/grna-design","genome-engineering/off-target-prediction","genome-engineering/base-editing-design","genome-engineering/prime-editing-design","genome-engineering/hdr-template-design"]
qc_checkpoints	[{"after_grna_design":"Context-valid on-target shortlist (CRISPOR: Rule Set 2 for U6/lentiviral, CRISPRscan for T7/embryo); reject TTTT and GC extremes; rank by predicted frameshift/out-of-frame fraction, not raw activity; carry 3-6 guides in an early constitutive NMD-competent exon"},{"after_offtarget":"Escalate predicted -> detected -> validated; reject guides with a low-mismatch high-CFD off-target in a gene; variant-aware (gnomAD) for therapeutic guides; high-fidelity nuclease in the delivery format used"},{"after_template":"Blocking (PAM/seed) mutation present AND codon-checked; edit within ~10 bp of the cut; donor format matches cell type (ssODN/lssDNA/dsDNA/AAV; HITI for post-mitotic); report edit:indel purity for base editing"}]
origin	openai4s
category	bioskills/workflows
metadata	{"tool_type":"mixed","primary_tool":"CRISPOR","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

Goal / edit	Modality	Route to
Gene knockout (any frameshift)	nuclease + NHEJ	grna-design (rank by frameshift fraction)
Knockout without a DSB / non-dividing / multiplex	base-editor premature stop or splice disruption	base-editing-design
CG->TA or AT->GC transition	base editing (CBE/ABE)	base-editing-design
C->G transversion	CGBE	base-editing-design
Other transversion, small indel, combined edit	prime editing	prime-editing-design
Small precise edit, no DSB tolerated	prime editing (PE)	prime-editing-design
Tag / reporter / allele replacement (cycling cells)	HDR knock-in	hdr-template-design
Large insertion / post-mitotic cells	HDR (AAV/HITI) or PE+integrase (PASTE/twinPE)	hdr-template-design / prime-editing-design

Error / symptom	Cause	Solution
Top guide has a near-perfect off-target	picked by on-target score alone	re-rank by specificity; on-target and specificity are separate axes
Efficient editing, no knockout phenotype	in-frame indels / late-exon / compensation	rank by frameshift fraction; target an early constitutive exon; verify protein
Base edit "80% efficient" but messy genotypes	bystanders in the window	report the spectrum; reposition or use a narrowed-window editor
HDR gives only indels	donor lacks a blocking mutation	add a codon-checked PAM/seed block; the edit was re-cut
Validation shows ~0% editing on a working edit	EDITED (or wrong) sequence supplied as `--amplicon_seq`, so edited reads match the reference / wrong guide window	give CRISPResso2 the UNEDITED reference as `--amplicon_seq` (+ `--expected_hdr_amplicon_seq` for HDR) and the actual `--guide_seq`
"No off-targets" claimed	LoD not stated / reference-only	state the LoD; variant-aware for therapeutics

bio-workflows-crispr-editing-pipeline

Opções de instalação

Revise os arquivos de origem

Version Compatibility

CRISPR Editing Pipeline

The Single Most Important Modern Insight -- the pipeline is a chain of handoffs, each with a checkpoint, and the pivotal decision is the edit modality

Version Compatibility

CRISPR Editing Pipeline

The Single Most Important Modern Insight -- the pipeline is a chain of handoffs, each with a checkpoint, and the pivotal decision is the edit modality

Edit-Modality Decision Tree (the pivotal branch)

Workflow Overview

Stage 1 -- Guide Design (-> grna-design)

Stage 2 -- Off-Target Assessment (-> off-target-prediction)

Stage 3 -- Modality-Specific Design

Stage 4 -- Validation

Common Errors (integration level)

References

Related Skills

bio-workflows-crispr-editing-pipeline

Informações da origem

Opções de instalação

Revise os arquivos de origem

Mais deste repositório

Mais deste repositório

Version Compatibility

CRISPR Editing Pipeline

The Single Most Important Modern Insight -- the pipeline is a chain of handoffs, each with a checkpoint, and the pivotal decision is the edit modality

Version Compatibility

CRISPR Editing Pipeline

The Single Most Important Modern Insight -- the pipeline is a chain of handoffs, each with a checkpoint, and the pivotal decision is the edit modality

Edit-Modality Decision Tree (the pivotal branch)

Workflow Overview

Stage 1 -- Guide Design (-> grna-design)

Stage 2 -- Off-Target Assessment (-> off-target-prediction)

Stage 3 -- Modality-Specific Design

Stage 4 -- Validation

Common Errors (integration level)

References

Related Skills